import torch
import torch.nn as nn
import numpy as np

class AsecondC(nn.Module):
    """
    AsecondC算子实现
    这是一个自定义的神经网络算子，用于特定的计算任务
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(AsecondC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        
        # 初始化权重
        self.weight = nn.Parameter(
            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
        )
        self.bias = nn.Parameter(torch.zeros(out_channels))
        
    def forward(self, x):
        """
        前向传播实现
        Args:
            x: 输入张量，形状为 [batch_size, in_channels, height, width]
        Returns:
            输出张量，形状为 [batch_size, out_channels, new_height, new_width]
        """
        # 实现AsecondC的核心计算逻辑
        batch_size = x.size(0)
        height = x.size(2)
        width = x.size(3)
        
        # 计算输出特征图的尺寸
        out_height = (height + 2 * self.padding - self.kernel_size) // self.stride + 1
        out_width = (width + 2 * self.padding - self.kernel_size) // self.stride + 1
        
        # 添加padding
        if self.padding > 0:
            x = nn.functional.pad(x, (self.padding,) * 4)
        
        # 自定义的计算逻辑
        output = torch.zeros(batch_size, self.out_channels, out_height, out_width,
                           device=x.device)
        
        # 这里实现AsecondC的特定计算逻辑
        # 示例：一个简化的实现
        for b in range(batch_size):
            for c_out in range(self.out_channels):
                for h in range(out_height):
                    for w in range(out_width):
                        h_start = h * self.stride
                        w_start = w * self.stride
                        h_end = h_start + self.kernel_size
                        w_end = w_start + self.kernel_size
                        
                        # 提取当前窗口的输入patch
                        patch = x[b, :, h_start:h_end, w_start:w_end]
                        
                        # 应用AsecondC特定的操作
                        # 这里使用简单的卷积操作作为示例
                        output[b, c_out, h, w] = torch.sum(
                            patch * self.weight[c_out]
                        ) + self.bias[c_out]
        
        return output
    
    def extra_repr(self):
        """返回额外的字符串表示"""
        return (f'in_channels={self.in_channels}, '
                f'out_channels={self.out_channels}, '
                f'kernel_size={self.kernel_size}, '
                f'stride={self.stride}, '
                f'padding={self.padding}') 